Further Development of the UND Pavement Precipitation Accumulation Estimation System: Complex Terrain Applications and Full Utilization of Clarus Data

Wintertime precipitation in regions of complex terrain, defined here as areas possessing terrain features or variations on scales < 10 km, pose special challenges for winter maintenance operations by state transportation departments. These challenges arise from a combination of the remoteness of the areas (with respect to relatively sparse, by comparison, observational networks) and the fact that the scales involved are not well represented within operational numerical weather prediction models or decision support tools utilizing such data. As a result it is often quite difficult to accurately estimate the amount of snow, ice, and freezing precipitation that accumulates on all roadways within a region characterized by complex terrain. The surface transportation ramifications include, at minimum, decreased public safety, compromised traveler mobility, and diminished productivity of roadway users.

An improved depiction of precipitation in complex terrain can be gained by fusing observations from multiple platforms together in a precipitation estimation system such as the one developed at the University of North Dakota. This system utilizes surface observations, radar, and, to a minor extent, geostationary satellite data streams in a manner to optimize the strengths of each data stream while mitigating their weaknesses. The complex terrain issues noted above require different approaches to the blending algorithms developed for simpler terrain environments.

Over the past year we have implemented methods to allow applications of the UND Pavement Precipitation Accumulation Estimation System (PPAES) within complex terrain, including new rule sets introduced for dealing with radar beam blockage and radar ray propagation as well as utilization of Rapid Refresh fields in areas where radar coverage is not available, which is particularly problematic in the Intermountain West and Alaska. Tests of our methodology will be conducted for selected case studies over a domain covering the state of Utah and adjacent areas of neighboring states. We also plan to perform a separate case study to demonstrate the benefits of full utilization of Clarus data within PPAES.